Overview

Dataset statistics

Number of variables12
Number of observations2988181
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory273.6 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical2

Alerts

click_timestamp is highly overall correlated with session_id and 1 other fieldsHigh correlation
session_id is highly overall correlated with click_timestamp and 1 other fieldsHigh correlation
session_start is highly overall correlated with click_timestamp and 1 other fieldsHigh correlation
click_environment is highly imbalanced (87.9%)Imbalance
click_deviceGroup is highly imbalanced (50.5%)Imbalance

Reproduction

Analysis started2024-05-17 22:05:21.687578
Analysis finished2024-05-17 22:10:36.903837
Duration5 minutes and 15.22 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

user_id
Real number (ℝ)

Distinct322897
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107947.83
Minimum0
Maximum322896
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:37.556865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6370
Q140341
median86229
Q3163261
95-th percentile274162
Maximum322896
Range322896
Interquartile range (IQR)122920

Descriptive statistics

Standard deviation83648.361
Coefficient of variation (CV)0.77489621
Kurtosis-0.46866505
Mean107947.83
Median Absolute Deviation (MAD)57248
Skewness0.72311891
Sum3.2256764 × 1011
Variance6.9970484 × 109
MonotonicityNot monotonic
2024-05-18T00:10:38.635044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5890 1232
 
< 0.1%
73574 939
 
< 0.1%
15867 900
 
< 0.1%
80350 783
 
< 0.1%
15275 746
 
< 0.1%
2151 722
 
< 0.1%
4568 529
 
< 0.1%
12897 513
 
< 0.1%
11521 502
 
< 0.1%
34541 501
 
< 0.1%
Other values (322887) 2980814
99.8%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 12
 
< 0.1%
2 4
 
< 0.1%
3 17
 
< 0.1%
4 7
 
< 0.1%
5 87
< 0.1%
6 35
< 0.1%
7 22
 
< 0.1%
8 56
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
322896 2
< 0.1%
322895 2
< 0.1%
322894 2
< 0.1%
322893 2
< 0.1%
322892 2
< 0.1%
322891 2
< 0.1%
322890 2
< 0.1%
322889 2
< 0.1%
322888 2
< 0.1%
322887 3
< 0.1%

session_id
Real number (ℝ)

HIGH CORRELATION 

Distinct1048594
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5074722 × 1015
Minimum1.5068254 × 1015
Maximum1.5082114 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:39.654097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5068254 × 1015
5-th percentile1.5069418 × 1015
Q11.5071242 × 1015
median1.5074933 × 1015
Q31.5077494 × 1015
95-th percentile1.5081532 × 1015
Maximum1.5082114 × 1015
Range1.3859559 × 1012
Interquartile range (IQR)6.2526185 × 1011

Descriptive statistics

Standard deviation3.8552446 × 1011
Coefficient of variation (CV)0.00025574233
Kurtosis-1.1113892
Mean1.5074722 × 1015
Median Absolute Deviation (MAD)3.3299497 × 1011
Skewness0.18075988
Sum3.5943168 × 1018
Variance1.4862911 × 1023
MonotonicityNot monotonic
2024-05-18T00:10:40.704085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.507563658 × 1015124
 
< 0.1%
1.507896573 × 1015107
 
< 0.1%
1.507133568 × 1015106
 
< 0.1%
1.507309773 × 101598
 
< 0.1%
1.508112331 × 101594
 
< 0.1%
1.507647366 × 101592
 
< 0.1%
1.507475404 × 101586
 
< 0.1%
1.506959499 × 101582
 
< 0.1%
1.508154737 × 101579
 
< 0.1%
1.506999909 × 101575
 
< 0.1%
Other values (1048584) 2987238
> 99.9%
ValueCountFrequency (%)
1.506825423 × 10152
< 0.1%
1.506825426 × 10152
< 0.1%
1.506825435 × 10152
< 0.1%
1.506825443 × 10152
< 0.1%
1.506825528 × 10152
< 0.1%
1.506825541 × 10153
< 0.1%
1.506825553 × 10152
< 0.1%
1.506825568 × 10152
< 0.1%
1.506825573 × 10153
< 0.1%
1.506825599 × 10152
< 0.1%
ValueCountFrequency (%)
1.508211379 × 10152
 
< 0.1%
1.508211376 × 10152
 
< 0.1%
1.508211372 × 10152
 
< 0.1%
1.508211369 × 10157
< 0.1%
1.508211367 × 10152
 
< 0.1%
1.508211353 × 10154
< 0.1%
1.508211348 × 10152
 
< 0.1%
1.508211326 × 10152
 
< 0.1%
1.508211326 × 10154
< 0.1%
1.508211324 × 10152
 
< 0.1%

session_start
Real number (ℝ)

HIGH CORRELATION 

Distinct646874
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5074722 × 1012
Minimum1.5068254 × 1012
Maximum1.5082114 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:41.720546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5068254 × 1012
5-th percentile1.5069418 × 1012
Q11.5071242 × 1012
median1.5074933 × 1012
Q31.5077494 × 1012
95-th percentile1.5081532 × 1012
Maximum1.5082114 × 1012
Range1.385956 × 109
Interquartile range (IQR)6.25262 × 108

Descriptive statistics

Standard deviation3.8552446 × 108
Coefficient of variation (CV)0.00025574233
Kurtosis-1.1113892
Mean1.5074722 × 1012
Median Absolute Deviation (MAD)3.32995 × 108
Skewness0.18075988
Sum4.5045999 × 1018
Variance1.4862911 × 1017
MonotonicityNot monotonic
2024-05-18T00:10:42.826336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.507563657 × 1012127
 
< 0.1%
1.507896573 × 1012112
 
< 0.1%
1.507133567 × 1012108
 
< 0.1%
1.507309773 × 101298
 
< 0.1%
1.507647366 × 101297
 
< 0.1%
1.508112331 × 101296
 
< 0.1%
1.506959499 × 101287
 
< 0.1%
1.507651543 × 101287
 
< 0.1%
1.507475403 × 101286
 
< 0.1%
1.508154737 × 101285
 
< 0.1%
Other values (646864) 2987198
> 99.9%
ValueCountFrequency (%)
1.506825423 × 10122
< 0.1%
1.506825426 × 10122
< 0.1%
1.506825435 × 10122
< 0.1%
1.506825442 × 10122
< 0.1%
1.506825528 × 10122
< 0.1%
1.506825541 × 10123
< 0.1%
1.506825553 × 10122
< 0.1%
1.506825568 × 10122
< 0.1%
1.506825573 × 10123
< 0.1%
1.506825599 × 10122
< 0.1%
ValueCountFrequency (%)
1.508211379 × 10122
 
< 0.1%
1.508211376 × 10122
 
< 0.1%
1.508211372 × 10122
 
< 0.1%
1.508211369 × 10127
< 0.1%
1.508211367 × 10122
 
< 0.1%
1.508211353 × 10124
< 0.1%
1.508211348 × 10122
 
< 0.1%
1.508211326 × 10122
 
< 0.1%
1.508211325 × 10124
< 0.1%
1.508211324 × 10122
 
< 0.1%

session_size
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9018851
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:43.957655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q34
95-th percentile9
Maximum124
Range122
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.9299415
Coefficient of variation (CV)1.0071905
Kurtosis158.46089
Mean3.9018851
Median Absolute Deviation (MAD)1
Skewness9.0900749
Sum11659539
Variance15.44444
MonotonicityNot monotonic
2024-05-18T00:10:45.019394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1260372
42.2%
3 670185
22.4%
4 374240
 
12.5%
5 220105
 
7.4%
6 135762
 
4.5%
7 88354
 
3.0%
8 58544
 
2.0%
9 40878
 
1.4%
10 29530
 
1.0%
11 21714
 
0.7%
Other values (62) 88497
 
3.0%
ValueCountFrequency (%)
2 1260372
42.2%
3 670185
22.4%
4 374240
 
12.5%
5 220105
 
7.4%
6 135762
 
4.5%
7 88354
 
3.0%
8 58544
 
2.0%
9 40878
 
1.4%
10 29530
 
1.0%
11 21714
 
0.7%
ValueCountFrequency (%)
124 124
< 0.1%
107 107
< 0.1%
106 106
< 0.1%
98 98
< 0.1%
94 94
< 0.1%
92 92
< 0.1%
86 86
< 0.1%
82 82
< 0.1%
79 79
< 0.1%
75 75
< 0.1%

click_article_id
Real number (ℝ)

Distinct46033
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194922.65
Minimum3
Maximum364046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:46.117181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile42223
Q1124228
median202381
Q3277067
95-th percentile336254
Maximum364046
Range364043
Interquartile range (IQR)152839

Descriptive statistics

Standard deviation90768.421
Coefficient of variation (CV)0.4656638
Kurtosis-0.9430459
Mean194922.65
Median Absolute Deviation (MAD)77632
Skewness-0.12343654
Sum5.8246416 × 1011
Variance8.2389063 × 109
MonotonicityNot monotonic
2024-05-18T00:10:47.125330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160974 37213
 
1.2%
272143 28943
 
1.0%
336221 23851
 
0.8%
234698 23499
 
0.8%
123909 23122
 
0.8%
336223 21855
 
0.7%
96210 21577
 
0.7%
162655 21062
 
0.7%
183176 20303
 
0.7%
168623 19526
 
0.7%
Other values (46023) 2747230
91.9%
ValueCountFrequency (%)
3 1
< 0.1%
27 1
< 0.1%
69 1
< 0.1%
81 2
< 0.1%
84 1
< 0.1%
94 2
< 0.1%
115 2
< 0.1%
125 1
< 0.1%
137 1
< 0.1%
139 1
< 0.1%
ValueCountFrequency (%)
364046 2
 
< 0.1%
364043 8
 
< 0.1%
364028 1
 
< 0.1%
364022 1
 
< 0.1%
364017 22
< 0.1%
364015 1
 
< 0.1%
364014 1
 
< 0.1%
364013 1
 
< 0.1%
364012 1
 
< 0.1%
364001 4
 
< 0.1%

click_timestamp
Real number (ℝ)

HIGH CORRELATION 

Distinct2983198
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5074743 × 1012
Minimum1.5068268 × 1012
Maximum1.5106035 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:48.154418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5068268 × 1012
5-th percentile1.5069432 × 1012
Q11.5071269 × 1012
median1.5074949 × 1012
Q31.507751 × 1012
95-th percentile1.5081552 × 1012
Maximum1.5106035 × 1012
Range3.7766549 × 109
Interquartile range (IQR)6.2415175 × 108

Descriptive statistics

Standard deviation3.8585096 × 108
Coefficient of variation (CV)0.00025595857
Kurtosis-1.0926869
Mean1.5074743 × 1012
Median Absolute Deviation (MAD)3.3326927 × 108
Skewness0.18427149
Sum4.504606 × 1018
Variance1.4888096 × 1017
MonotonicityNot monotonic
2024-05-18T00:10:49.232608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.507554094 × 10123
 
< 0.1%
1.507984106 × 10123
 
< 0.1%
1.507905589 × 10123
 
< 0.1%
1.506975362 × 10123
 
< 0.1%
1.507052449 × 10123
 
< 0.1%
1.508164975 × 10123
 
< 0.1%
1.50696101 × 10123
 
< 0.1%
1.507320444 × 10123
 
< 0.1%
1.506956077 × 10123
 
< 0.1%
1.507550142 × 10122
 
< 0.1%
Other values (2983188) 2988152
> 99.9%
ValueCountFrequency (%)
1.5068268 × 10121
< 0.1%
1.506826802 × 10121
< 0.1%
1.506826804 × 10121
< 0.1%
1.506826814 × 10121
< 0.1%
1.506826819 × 10121
< 0.1%
1.506826823 × 10121
< 0.1%
1.506826828 × 10121
< 0.1%
1.50682683 × 10121
< 0.1%
1.506826831 × 10121
< 0.1%
1.506826832 × 10121
< 0.1%
ValueCountFrequency (%)
1.510603455 × 10121
< 0.1%
1.510603425 × 10121
< 0.1%
1.510093913 × 10121
< 0.1%
1.510093883 × 10121
< 0.1%
1.509798423 × 10121
< 0.1%
1.509798393 × 10121
< 0.1%
1.509736674 × 10121
< 0.1%
1.509709959 × 10121
< 0.1%
1.50956166 × 10121
< 0.1%
1.509561584 × 10121
< 0.1%

click_environment
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
4
2904478 
2
 
79743
1
 
3960

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2988181
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

Length

2024-05-18T00:10:50.240718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T00:10:50.892612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 2904478
97.2%
2 79743
 
2.7%
1 3960
 
0.1%

click_deviceGroup
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
1
1823162 
3
1047086 
4
 
117640
5
 
283
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2988181
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

Length

2024-05-18T00:10:51.814744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T00:10:52.634885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2988181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1823162
61.0%
3 1047086
35.0%
4 117640
 
3.9%
5 283
 
< 0.1%
2 10
 
< 0.1%

click_os
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.277603
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:53.345167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median17
Q317
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)15

Descriptive statistics

Standard deviation6.8817184
Coefficient of variation (CV)0.51829523
Kurtosis-0.93175147
Mean13.277603
Median Absolute Deviation (MAD)0
Skewness-0.95411713
Sum39675882
Variance47.358048
MonotonicityNot monotonic
2024-05-18T00:10:54.088672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
17 1738138
58.2%
2 788699
26.4%
20 369586
 
12.4%
12 60096
 
2.0%
13 23711
 
0.8%
19 6384
 
0.2%
5 1513
 
0.1%
3 54
 
< 0.1%
ValueCountFrequency (%)
2 788699
26.4%
3 54
 
< 0.1%
5 1513
 
0.1%
12 60096
 
2.0%
13 23711
 
0.8%
17 1738138
58.2%
19 6384
 
0.2%
20 369586
 
12.4%
ValueCountFrequency (%)
20 369586
 
12.4%
19 6384
 
0.2%
17 1738138
58.2%
13 23711
 
0.8%
12 60096
 
2.0%
5 1513
 
0.1%
3 54
 
< 0.1%
2 788699
26.4%

click_country
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.357656
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:55.230157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.725861
Coefficient of variation (CV)1.2712063
Kurtosis21.55276
Mean1.357656
Median Absolute Deviation (MAD)0
Skewness4.8022523
Sum4056922
Variance2.9785961
MonotonicityNot monotonic
2024-05-18T00:10:55.946326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2852406
95.5%
10 61377
 
2.1%
11 29999
 
1.0%
8 9556
 
0.3%
6 7256
 
0.2%
9 6746
 
0.2%
2 6101
 
0.2%
3 4540
 
0.2%
5 3498
 
0.1%
4 3389
 
0.1%
ValueCountFrequency (%)
1 2852406
95.5%
2 6101
 
0.2%
3 4540
 
0.2%
4 3389
 
0.1%
5 3498
 
0.1%
6 7256
 
0.2%
7 3313
 
0.1%
8 9556
 
0.3%
9 6746
 
0.2%
10 61377
 
2.1%
ValueCountFrequency (%)
11 29999
1.0%
10 61377
2.1%
9 6746
 
0.2%
8 9556
 
0.3%
7 3313
 
0.1%
6 7256
 
0.2%
5 3498
 
0.1%
4 3389
 
0.1%
3 4540
 
0.2%
2 6101
 
0.2%

click_region
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.313314
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:56.698839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q113
median21
Q325
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0640064
Coefficient of variation (CV)0.38573064
Kurtosis-0.97550782
Mean18.313314
Median Absolute Deviation (MAD)4
Skewness-0.54588002
Sum54723498
Variance49.900187
MonotonicityNot monotonic
2024-05-18T00:10:57.593186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25 804985
26.9%
21 464230
15.5%
13 320957
 
10.7%
8 179339
 
6.0%
16 164884
 
5.5%
28 135793
 
4.5%
24 130537
 
4.4%
20 120884
 
4.0%
5 96979
 
3.2%
9 84693
 
2.8%
Other values (18) 484900
16.2%
ValueCountFrequency (%)
1 7110
 
0.2%
2 16728
 
0.6%
3 3997
 
0.1%
4 30265
 
1.0%
5 96979
3.2%
6 57254
 
1.9%
7 64062
 
2.1%
8 179339
6.0%
9 84693
2.8%
10 21995
 
0.7%
ValueCountFrequency (%)
28 135793
 
4.5%
27 18711
 
0.6%
26 18893
 
0.6%
25 804985
26.9%
24 130537
 
4.4%
23 43
 
< 0.1%
22 13101
 
0.4%
21 464230
15.5%
20 120884
 
4.0%
19 34092
 
1.1%

click_referrer_type
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8389813
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2024-05-18T00:10:58.517635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1563557
Coefficient of variation (CV)0.62880232
Kurtosis9.1175335
Mean1.8389813
Median Absolute Deviation (MAD)0
Skewness2.8399665
Sum5495209
Variance1.3371585
MonotonicityNot monotonic
2024-05-18T00:10:59.265911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1602601
53.6%
1 1194321
40.0%
5 80766
 
2.7%
7 69798
 
2.3%
6 20455
 
0.7%
4 19820
 
0.7%
3 420
 
< 0.1%
ValueCountFrequency (%)
1 1194321
40.0%
2 1602601
53.6%
3 420
 
< 0.1%
4 19820
 
0.7%
5 80766
 
2.7%
6 20455
 
0.7%
7 69798
 
2.3%
ValueCountFrequency (%)
7 69798
 
2.3%
6 20455
 
0.7%
5 80766
 
2.7%
4 19820
 
0.7%
3 420
 
< 0.1%
2 1602601
53.6%
1 1194321
40.0%

Interactions

2024-05-18T00:09:52.757277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:42.750181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:57.342292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:12.146726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:27.444033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:42.009940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:56.908820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:10.755505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:23.561842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:35.517615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:54.421781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:44.258123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:58.736120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:13.783309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:28.994036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:43.337559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:58.434443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:12.035904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:24.738036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:37.110552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:56.140849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:45.824928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:00.174158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:15.372205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:30.747524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:44.808976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:59.846671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:13.488766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:25.930668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:38.731284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:57.818542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:47.262569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:01.698642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:16.835599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:32.512390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:46.370933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:01.284126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:14.855259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:27.116333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:40.608801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:59.310977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:48.769001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:03.137845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:18.442920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:33.919119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:47.710063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:02.603807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:16.181696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:28.257500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:42.322935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:10:00.776845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:50.308784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:05.160939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:20.073198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:35.233620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:49.375340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:04.294981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:17.501535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:29.571946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:44.314864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:10:02.282528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:51.777219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:06.580617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:21.644150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:36.569676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:50.822637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:05.579832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:18.729976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:30.745543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:46.250681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:10:03.826359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:53.234038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:08.022758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:23.280819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:37.935130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:52.192802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:06.946026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:19.986982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:31.942399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:48.379508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:10:05.681924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:54.598240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:09.370925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:24.782972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:39.378683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:53.680054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:08.183101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:21.184268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:33.042880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:49.750659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:10:07.050015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:07:55.970181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:10.762053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:26.086954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:40.648640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:08:55.237043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:09.413754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:22.390510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:34.188094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T00:09:51.196808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-18T00:10:59.903167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
click_article_idclick_countryclick_deviceGroupclick_environmentclick_osclick_referrer_typeclick_regionclick_timestampsession_idsession_sizesession_startuser_id
click_article_id1.000-0.0090.0280.063-0.0020.0470.1090.0620.062-0.0210.062-0.000
click_country-0.0091.0000.0500.0470.0800.0210.365-0.006-0.006-0.012-0.006-0.034
click_deviceGroup0.0280.0501.0000.325-0.3210.0530.0460.0250.028-0.1600.028-0.043
click_environment0.0630.0470.3251.000-0.001-0.041-0.031-0.007-0.0080.033-0.0080.022
click_os-0.0020.080-0.321-0.0011.000-0.0010.037-0.020-0.0210.070-0.021-0.027
click_referrer_type0.0470.0210.053-0.041-0.0011.0000.030-0.017-0.016-0.313-0.0160.067
click_region0.1090.3650.046-0.0310.0370.0301.0000.0010.001-0.0170.001-0.024
click_timestamp0.062-0.0060.025-0.007-0.020-0.0170.0011.0000.9990.0080.9990.252
session_id0.062-0.0060.028-0.008-0.021-0.0160.0010.9991.0000.0021.0000.253
session_size-0.021-0.012-0.1600.0330.070-0.313-0.0170.0080.0021.0000.002-0.156
session_start0.062-0.0060.028-0.008-0.021-0.0160.0010.9991.0000.0021.0000.253
user_id-0.000-0.034-0.0430.022-0.0270.067-0.0240.2520.253-0.1560.2531.000

Missing values

2024-05-18T00:10:08.059349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T00:10:15.713882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idsession_idsession_startsession_sizeclick_article_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_type
0125902150764618116443615076461810003300082150764620539041171251
1125902150764618116443615076461810003157339150764624067841171251
2125902150764618116443615076461810003314424150764627067841171251
37237150764618183443715076461810002156964150764631638641171212
4723715076461818344371507646181000264409150764634638641171212
55557515076461821604381507646182000264409150764618387341171255
655575150764618216043815076461820002235870150764621387341171255
7130739150764618224043915076461820002236338150764694644341171212
8130739150764618224043915076461820002336221150764697644341171212
977669150764618372344015076461830002234698150764744542641171252
user_idsession_idsession_startsession_sizeclick_article_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_type
298817157183150726455314747715072645530003162016150726671634943201241
298817257183150726455314747715072645530003298553150726737243443201241
298817357183150726455314747715072645530003158509150726740243443201241
29881746955015072645641084781507264564000227697015072649736464321211
29881756955015072645641084781507264564000212417715072650036464321211
298817686958150726456439647915072645640003254949150726469643743210281
298817786958150726456439647915072645640003235804150726477117343210281
298817886958150726456439647915072645640003236207150726480117343210281
298817927806150726457527648015072645750002207797150726507210041171252
298818027806150726457527648015072645750002161425150726510210041171252